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The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

The purpose of this paper is to determine the effect of clothing fabrics, sizes and air ventilation rate on the volume and thickness of the air gap under the air ventilation garments (AVGs).

The geometric models of the human body and clothing were obtained by using a 3D body scanner. Then the distribution of the volume and thickness of the air gap for four clothing fabrics and three air ventilation rates (0L/S, 12L/S and 20L/S) were calculated by Geomagic software. Finally, a more suitable fabric was selected from the analysis to compare the distribution of the air gap entrapped for four clothing sizes (S, M, L and XL) and the three air ventilation rates.

The results show that the influence of air ventilation rate on the air gap volume and thickness is more obvious than that of the clothing fabrics and sizes. The higher is the air ventilation rate, the thicker is the air gap entrapped, and more evenly distributed is the air gap. It can be seen that the thickness of the air gap in the chest does not change significantly with the changes of the air ventilation rates, clothing fabrics and sizes, while the air gap in the waist is affected significantly.

This research provides a better understanding of the distribution of the air gap entrapped in ventilated garments, which can help in designing the optimal air gap dimensions and thus provide a basis and a reference for the design of the AVGs.

This study aims to investigate the strategic priorities of green building projects and analyze energy consumption alternatives in green residence projects using two innovative methods.

This study uses two methods, decision-making trial and evaluation laboratory (DEMATEL) to measure strategic priorities and golden-cut quantum spherical fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) to analyze energy consumption alternatives.

The study reveals that sustainability and atmosphere are the most significant factors in determining the priorities of green residence projects, whereas innovation has a limited impact on addressing environmental challenges in the building sector. The ranking of energy use alternatives shows that sustainability issues and atmosphere quality of space heating and cooking are the top priorities, whereas other factors like white goods, water heating, lighting and space cooling are ranked lower.

This paper offers a significant contribution to the understanding of green buildings by introducing innovative methodological approaches. Theoretically, it uses the DEMATEL to enhance traditional analytical frameworks, marking a novel effort in understanding green residence projects. In addition, the golden-cut quantum spherical fuzzy TOPSIS method is introduced, offering a comprehensive decision-making framework for green projects, considering factors like energy consumption and economic feasibility. This combination of methodologies provides a holistic evaluation, emphasizing sustainability in green building construction. This study reveals untapped potential for environmental sustainability and energy efficiency, enriching the existing knowledge base.

The purpose of this paper is to develop and demonstrate a comprehensive 3D spatio-temporal maintenance management model for high-rise residential buildings by integrating Industry 4.0 technologies and lean maintenance principles. This model aims to optimize maintenance scheduling, enhance resource utilization and improve decision-making processes. By leveraging advanced data visualization and predictive analytics, this study seeks to address the complexities of building maintenance, ensure timely interventions, reduce downtime and extend the lifespan of building assets, ultimately leading to more efficient and sustainable maintenance management practices.

Integrating state-of-the-art technologies such as big data analytics and artificial intelligence into the proposed model is geared towards benefiting from optimized maintenance scheduling and resource allocation, hence achieving minimum asset downtime and extension in asset life. This is being done through the digitization of paper maps, the development of 3D building models in AutoCAD and SketchUp and the placing of the developed models into ArcGIS Pro. The PostgreSQL database with PostGIS extension supports optimal storage and management of spatial data towards real-time updates and advanced analyses.

The results revealed that the model enhances maintenance planning considerably better than traditional methods due to the revelation of meaningful patterns and trends that are not visible in conventional visualization methods. Temporal analysis indicates increasing needs for maintenance through time, whereas spatial analysis can point out the units that require special attention. The spatiotemporal analysis is needed to determine overall maintenance requirements for better decision-making. The work demonstrated that 3D visualization of maintenance activities performed over building representation helps facility managers in better decision-making related to task planning for performance improvement concerning building and tenant satisfaction.

The study’s current limitations include the reliance on specific datasets and technologies, which may need adaptation for broader applications. Future research could explore further integration with additional building types and longitudinal studies to assess long-term impacts.

The 3D visualization of maintenance activities over building representation aids facility managers in better decision-making related to task planning, improving building performance and tenant satisfaction. This integrated approach provides significant benefits in efficiency, resource use and sustainability.

The originality of this paper lies in its innovative integration of 3D spatio-temporal data with Industry 4.0 technologies and lean maintenance principles to create a comprehensive maintenance management model for high-rise residential buildings. Unlike traditional approaches, this model combines advanced data visualization, real-time analytics and predictive maintenance strategies within a unified geographic information system framework. This holistic approach not only enhances maintenance planning and resource allocation but also provides a proactive, data-driven methodology that significantly improves the efficiency and effectiveness of maintenance management, addressing the unique challenges of high-rise residential building maintenance.

One of the simplest ways to improve the profitability of a business is by saving energy. Responding to recent calls to investigate the mechanism leading to individuals’ energy-saving behaviors in the workplace, this study aims to investigate combining person-environment (PE) fit theory with normative factors to understand employees’ decisions to engage in energy-saving activities.

The results of an online survey reveal that person-organization fit, person-job fit and moral norm significantly affect employees’ energy-saving intention.

Furthermore, the findings show that moral norm is the strongest predictor of employees’ willingness to save energy. The results also demonstrate the interrelated relationships between PE fit and normative factors.

The results contribute overall to a greater understanding of energy-saving practices in the organizational context. Apart from the theoretical contributions, the findings of the current investigation offer valuable practical insights for organizations and policymakers to promote energy conservation practices.

The purpose of this paper is to provide a critical historical analysis of the business (mis)behaviors and influencing factors that discourage enduring cooperation between principals and agents, to introduce strategies that embrace the social values, economic motivation and institutional designs historically adopted to curtail dishonest acts in international business and to inform an improved principal–agent theory that reflects principal–agent reciprocity as shaped by social, political, cultural, economic, strategic and ideological forces

The critical historical research method is used to analyze Chinese compradors and the foreign companies they served in pre-1949 China.

Business practitioners can extend orthodox principal–agent theory by scrutinizing the complex interactions between local agents and foreign companies. Instead of agents pursuing their economic interests exclusively, as posited by principal–agent theory, they also may pursue principal-shared interests (as suggested by stewardship theory) because of social norms and cultural values that can affect business-related choices and the social bonds built between principals and agents.

The behaviors of compradors and foreign companies in pre-1949 China suggest international business practices for shaping social bonds between principals and agents and foreign principals’ creative efforts to enhance shared interests with local agents.

Understanding principal–agent theory’s limitations can help international management scholars and practitioners mitigate transaction partners’ dishonest acts.

A critical historical analysis of intermediary businesspeople’s (mis)behavior in pre-1949 (1840–1949) China can inform the generalizability of principal–agent theory and contemporary business strategies for minimizing agents’ dishonest acts.

Eco-innovations (EIs) are intended to benefit not only the environment but society and firms, but how the relationship is reconciled is unclear, particularly in emerging economies. The advancement of EI has resulted in both positive and negative relationships with sustainability, indicating that the association is more complex than a simple linear one.

Thus, the authors hypothesize that EI has a curvilinear relationship with sustainable business performance (SPB) and that market turbulence (MT) exerts stimulus that reinforces EIs. Accordingly, using the Stata software, the authors apply a moderated regression to a sample size data of 511 manufacturing firms to test the hypothesized assumptions.

Although the results attest to a positive relationship between EI and SBP, the results are synonymous with an inverted “U” shape that renders EIs unprofitable beyond a certain threshold (rebound effect). Additionally, the authors observe that the moderation stimulus of technology turbulence flattens the inverted U-shaped curve.

Built on the foundations of natural-resource-based view (NRBV) and contingency theory, the authors identify the rebound effect point of EI and SBP and the reinforcing stimulus of MT.